As the power system develops from informatization to intelligence. Research on data services based on the Internet of Things (IoT) focuses more on application functions, but the research on the data quality of the IoT itself is insufficient. Long-term continuous operation of the big data IoT system has the risk of performance degradation or even partial fault, which leads to a decrease in the availability of collected data for intelligent analysis. In this article, based on the power IoT message data, the characteristics are established through a variety of improved detection methods, and then the abnormal data type is obtained through $Q$ learning and fusion of the random forest (RF) identification features. Finally, the topology of the specific power user IoT system is combined with kernel principal component analysis (KPCA) + improved RF algorithm getting the abnormal location of the IoT. The results show that the research method has a significantly higher positioning accuracy (from 61% to 97%) than the traditional RF method, and the combination method has more advantages in parameter adjustment and classification accuracy than directly using a multilayer perceptron (MLP).
[1]
Hongseok Kim,et al.
Short-Term Load Forecasting based on ResNet and LSTM
,
2018,
2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm).
[2]
Tan Jun.
A Method of Image De-noising Based KPCA
,
2004
.
[3]
Yu Hui-xin.
The Establishment and Analysis of the Time Series Modelof China's GDP Per Capita
,
2000
.
[4]
ChenLei,et al.
Universal approximation using incremental constructive feedforward networks with random hidden nodes
,
2006
.
[5]
Weijie Kang,et al.
Research on Data-Driven Fault Diagnosis Technology of Cloud Test
,
2020
.
[6]
C. L. Philip Chen,et al.
Broad learning system: A new learning paradigm and system without going deep
,
2017,
2017 32nd Youth Academic Annual Conference of Chinese Association of Automation (YAC).